中国省级环境决策支持系统的系统分析

中国省级环境决策支持系统(DSS)是基于GIS和各种模型的空间决策支持系统?该文介绍了DSS系统的系统分析方法和内容,包括软件需求说明?数据需求说明和总体设计      

浅谈环境信息中的GIS技术和中介数据

介绍了在环境信息系统中GIS技术所具有的功能和中介数据的作用及其规范化问题;并结合中国省级环境决策支持系统的开发,介绍了它们在环境信息中的使用。      

城市大气自动监测系统监测资料的处理及其应用

本文讨论城市大气自动监测系统资料处理及其应用的某些问题。总结了对数正态分布、指数分布、Г分布和Ｗｅｉｂｕｌｌ分布等分布模型对监测资料分布拟合的适用性及分布模型拟合判别指标。介绍了监测资料在监测时空代表性分析、污染物浓度预防研究方面的应用途径。     

小清河沿岸地下水中有机污染物优先排序的研究

提出了对区域性有机污染物优先排序和风险分类的定量评价方法———密切值法。该方法以反映污染物的毒性、暴露水平和化学性质的6项参数为指标,对小清河沿岸地下水中的有机污染物进行了优先排序和风险分类。      

Testing the importance of accurate meteorological input fields and parameterizations in atmospheric transport modelling using dream - validation against ETEX-1

A tracer model, the DREAM, which is based on a combination of a near-range Lagrangian model and a long-range Eulerian model, has been developed. The meteorological meso-scale model, MM5V1, is implemented as a meteorological driver for the tracer model. The model system is used for studying transport and dispersion of air pollutants caused by a single but strong source as, e.g. an accidental release from a nuclear power plant. The model system including the coupling of the Lagrangian model with the Eulerian model are described. Various simple and comprehensive parameterizations of the mixing height, the vertical dispersion, and different meterological input data have been implemented in the combined tracer model, and the model results have been validated against measurements from the ETEX-1 release. Several different statistical parameters have been used to estimate the differences between the parameterizations and meterological input data in order to find the best performing solution.     

GIS在水污染控制中的研究与探讨

地理信息系统(GIS)是专门用于地理空间信息处理和管理的计算机技术系统，造成城市水污染的污染源及其相关数据均具有空间分布特性，这决定了GIS可在该领域发挥重要作用。GIS能支持与水环境有关的地理空间数据的获取、管理、分析、模拟和显示，以解决复杂的水环境综合治理问题和水污染控制问题；同时，建立完善的空间数据库和属性数据库，为环境保护部门和科研部门提供研究与决策支持。本文就GIS在水污染控制这一领域的应用做一定探讨。     

Occupational risks and the value and modelling of a measurement of severity

In studies of occupational risks, severity, which is a component of the estimation of every risk, appears as a multifaceted entity assessable according to numerous criteria. A method of measuring the degree of severity of the consequences of potentially dangerous events would be of undeniable value to organisations seeking to improve their understanding of the complexity of such events. The need to control severity is highlighted by scientifically acquired improvements in the understanding of occupational risks, by certain new regulatory obligations in Europe, and by some requirements in the financial management of organisations. We put forward a statistical way of integrating several constituent elements of severity and hence of determining a relevant, synthetic, one-dimensional index. This is achieved by means of principal component analysis (PCA), which is used here to calculate a resultant severity, as in some physical measurements. We also investigate how severity may be statistically modelled, with the aim of contributing to the quantitative assessment of occupational risks. The choice of parametric models is detailed and illustrated by the search for a suitable model for workplace accidents in an organisational setting. The practical value of modelling severity is two-fold. First, one is able to study the distribution of the numerical values of severity over a continuum (a theoretically infinite numerical set) rather than through a limited number of arbitrarily defined categories. Second, with a generally applicable parametric model, one can estimate the law of probability of a measurement of severity in a particular situation, notably recent or new. Lastly, the statistical concept of risk curve is defined and discussed. The goal is to incorporate the severity component into the risk assessment in the form of a probability law, thus circumventing the difficulties associated with an analysis of scenarios.     

RESEARCH: Comparison of Temporal Trends in Ambient and Compliance Trace Element and PCB Data in Pool 2 of the Mississippi River, USA, 1985–1995

6+ ), copper (Cu), lead (Pb), mercury (Hg), nickel (Ni), selenium (Se), zinc (Zn), and polychlorinated biphenyls (PCBs). Water-column, bed-sediment, and fish-tissue (fillets) data collected by five government agencies comprised the ambient data set; effluent data from five registered facilities comprised the compliance data set. The nonparametric Mann-Kendall trend test indicated that 33% of temporal trends in all data were statistically significant (P < 0.05). Possible reasons for this were low sample sizes, and a high percentage of samples below the analytical detection limit. Trends in compliance data were more distinct; most trace elements decreased significantly, probably due to improvements in wastewater treatment. Seven trace elements (Cr, Cd, Cu, Pb, Hg, Ni, and Zn) had statistically significant decreases in wastewater and portions of either or both ambient water and bed sediment. No trends were found in fish tissue. Inconsistency in trends between ambient and compliance data were often found for individual constituents, making overall similarity between the data sets difficult to determine. Logistical differences in monitoring programs, such as varying field and laboratory methods among agencies, made it difficult to assess ambient temporal trends.     

Comparing discriminant analysis, neural networks and logistic regression for predicting species distributions: a case study with a Himalayan river bird

We assessed the occurrence of a common river bird, the Plumbeous Redstart Rhyacornis fuliginosus, along 180 independent streams in the Indian and Nepali Himalaya. We then compared the performance of multiple discrimant analysis (MDA), logistic regression (LR) and artificial neural networks (ANN) in predicting this species’ presence or absence from 32 variables describing stream altitude, slope, habitat structure, chemistry and invertebrate abundance. Using the entire data (=training set) and a threshold for accepting presence in ANN and LR set to P≥0.5, ANN correctly classified marginally more cases (88%) than either LR (83%) or MDA (84%). Model performance was assessed from two methods of data partitioning. In a ‘leave-one-out’ approach, LR correctly predicted more cases (82%) than MDA (73%) or ANN (69%). However, in a holdout procedure, all the methods performed similarly (73–75%). All methods predicted true absence (i.e. specificity in holdout: 81–85%) better than true presence (i.e. sensitivity: 57–60%). These effects reflect species’ prevalence (=frequency of occurrence), but are seldom considered in distribution modelling. Despite occurring at only 36% of the sites, Plumbeous Redstarts are one of the most common Himalayan river birds, and problems will be greater with less common species. Both LR and ANN require an arbitrary threshold probability (often P=0.5) at which to accept species presence from model prediction. Simulations involving varied prevalence revealed that LR was particularly sensitive to threshold effects. ROC plots (received operating characteristic) were therefore used to compare model performance on test data at a range of thresholds; LR always outperformed ANN. This case study supports the need to test species’ distribution models with independent data, and to use a range of criteria in assessing model performance. ANN do not yet have major advantages over conventional multivariate methods for assessing bird distributions. LR and MDA were both more efficient in the use of computer time than ANN, and also more straightforward in providing testable hypotheses about environmental effects on occurrence. However, LR was apparently subject to chance significant effects from explanatory variables, emphasising the well-known risks of models based purely on correlative data.